AI-powered IoT for COVID-19

AI-powered IoT for COVID-19

The Internet of Things (IoT) has made revolutionary advances in the utility grid as we know it. Among these advances, intelligent medical services are gaining much interest. The use of Artificial Intelligence (AI) is increasing day after day in fighting one of the most significant viruses, COVID-19....

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Detalles Bibliográficos
Autor principal: Al-Turjman, Fadi (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press 2021.
Edición:First edition
Materias:
COVID-19 (Disease)
Artificial intelligence > Medical applications.
Internet of things > Health aspects.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009645680106719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Table of Contents
  • Preface
  • About the Author
  • List of Contributors
  • Chapter 1 Cloud Computing and Business Intelligence in IoT-Enabled Smart and Healthy Cities
  • 1.1 Introduction
  • 1.1.1 Comparison to Other Surveys
  • 1.1.2 Scope of the Paper and Its Contribution
  • 1.2 Cloud Computing
  • 1.2.1 Cloud Computing Characteristics
  • 1.2.2 Cloud Computing Models
  • 1.2.2.1 Infrastructure as a Service (IaaS)
  • 1.2.2.2 Platform as a Service (PaaS)
  • 1.2.2.3 Software as a Service (SaaS)
  • 1.2.3 Cloud Deployment Model
  • 1.2.3.1 Public Cloud
  • 1.2.3.2 Private Cloud
  • 1.2.3.3 Hybrid Cloud
  • 1.2.3.4 Community Cloud
  • 1.2.4 Restrictions and Opportunities of Cloud Computing
  • 1.2.5 Cloud Computing Qualities
  • 1.2.5.1 Storage over the Internet
  • 1.2.5.2 Service over Internet
  • 1.2.5.3 Application Used over Internet
  • 1.2.5.4 Energy Efficiencies
  • 1.3 Cloud Computing (CC) and Business Intelligence (BI)
  • 1.3.1 Business Intelligence
  • 1.3.2 Cloud-Based BI Model
  • 1.3.3 Cloud Computing Role in Business Intelligence
  • 1.3.4 Benefits and Difficulties of Cloud Computing in BI
  • 1.3.5 Incorporating BI Software into the Cloud
  • 1.4 Procedures in BI and the Cloud
  • 1.4.1 Collection of Data
  • 1.4.2 Effectiveness and Authenticity
  • 1.4.3 Preparation of Data and Data Analysis
  • 1.4.4 Data Surveys
  • 1.5 Information Technology in the Cloud and BI
  • 1.5.1 BI On-Request
  • 1.5.2 Cloud Speed Up Development
  • 1.5.3 Business Intelligence Prospects
  • 1.6 Artificial Intelligence (AI) in Business Intelligence (BI)
  • 1.6.1 Supervised Learning
  • 1.6.2 Unsupervised Learning
  • 1.6.3 Reinforcement Learning (Semi-Supervised)
  • 1.7 The Application Of Cloud IoT in Battling COVID-19
  • 1.7.1 Main Application Area of Cloud IoT in the COVID-19 Pandemic
  • 1.8 Security in BI.
  • 1.8.1 Customer Verifications
  • 1.8.2 Safeguarding Data
  • 1.8.3 Data Violation, Loss, and Destruction
  • 1.8.4 Storage Topography
  • 1.8.5 Public Cloud versus Private Cloud
  • 1.8.6 Version Awareness and Backup Execution
  • 1.8.7 Security of Data and Encoding
  • 1.9 Potential Hazards of Cloud BI Adoption
  • 1.9.1 Security Difficulties
  • 1.9.2 Threats and Risks in Cloud Computing
  • 1.9.3 Privacy and Security
  • 1.10 Discussion and Open Research
  • 1.11 Conclusion
  • Acknowledgments
  • References
  • Chapter 2 Resource Allocation in Volunteered Cloud Computing and Battling COVID-19
  • 2.1 Introduction
  • 2.1.1 Comparison to Other Surveys
  • 2.1.2 Scope of the Paper and Contributions
  • 2.2 Roles of Any Entity in the Cloud
  • 2.2.1 Provider
  • 2.2.2 User
  • 2.2.3 Vendor
  • 2.3 Resources in Cloud Volunteer Computing
  • 2.3.1 Storage-Based Resource
  • 2.3.1.1 Network Storage
  • 2.3.1.2 Hard Disk
  • 2.3.2 Network-Based Resource
  • 2.3.2.1 Reliable Communications
  • 2.3.2.2 Efficient Communications
  • 2.3.2.3 Virtual Networking
  • 2.3.3 Service Model as a Resource
  • 2.3.3.1 Infrastructure as a Service (IaaS)
  • 2.3.3.2 Software as a Service (IaaS)
  • 2.3.3.3 Platform as a Service (PaaS)
  • 2.3.4 Server as a Resource
  • 2.3.4.1 Cloud Server
  • 2.3.4.2 Dedicated Server
  • 2.3.5 MapReduce as a Resource
  • 2.3.6 Intelligence and Resource Allocation
  • 2.3.6.1 Supervised Learning
  • 2.3.6.2 Unsupervised Learning
  • 2.3.6.3 Reinforcement Learning (Semi-Supervised)
  • 2.4 Scheduling of Resources in the Cloud
  • 2.4.1 Cloud Service Scheduling
  • 2.4.2 Customer Side Scheduling
  • 2.4.3 Static and Dynamic Scheduling
  • 2.4.4 Heuristic Scheduling
  • 2.4.5 Workflow Scheduling
  • 2.4.6 Real-Time Scheduling
  • 2.4.7 Dynamic Dedicated Server Scheduling (DDSS)
  • 2.5 Algorithms of Resource Allocation
  • 2.5.1 Ant Colony Optimization Algorithm
  • 2.5.2 Bees Algorithm.
  • 2.5.3 Priority Algorithm
  • 2.5.4 Bin-Packing Algorithm
  • 2.6 Usage of Cloud and IoT in COVID-19
  • 2.6.1 Application of Cloud Service
  • 2.6.2 Health Care Segment
  • 2.6.3 Tracking the Spread
  • 2.6.4 Diagnosis
  • 2.7 Security of the Allocation of Resources
  • 2.7.1 Security Aspect
  • 2.7.1.1 Confidentiality and Authentication
  • 2.7.1.2 Privacy
  • 2.7.1.3 Time-Necessity
  • 2.7.1.4 Availability
  • 2.7.1.5 Trust
  • 2.7.1.6 Predication and Intrusion Detection
  • 2.7.2 Security Issues and Challenges
  • 2.7.2.1 Data Protection in Cloud Environments
  • 2.7.2.2 Authentication of User and Management Access
  • 2.7.2.3 Lack of Visibility of Cloud Services
  • 2.7.2.4 Absence of Control over Cloud Infrastructure
  • 2.8 Open Research and Discussion
  • 2.9 Conclusion
  • References
  • Chapter 3 Analyzing Radiographs for COVID-19 Using Artificial Intelligence
  • 3.1 Introduction
  • 3.2 Related Work
  • 3.3 Dataset
  • 3.4 Data Augmentation
  • 3.5 Models
  • 3.5.1 VGG-16
  • 3.5.2 VGG-19
  • 3.5.3 Custom Model
  • 3.5.4 SVM
  • 3.5.4.1 Grey Level Co-Occurrence Matrix
  • 3.5.4.2 Grey Level Size Zone Matrix
  • 3.5.4.3 Discrete Wavelet Transform
  • 3.5.5 Modified CNN
  • 3.5.5.1 Input Layer
  • 3.5.5.2 Convolution Layer
  • 3.5.5.3 Batch Normalization Layer
  • 3.5.5.4 Rectified Linear Unit Layer
  • 3.5.5.5 Fully Connected layer
  • 3.5.5.6 SoftMax Layer
  • 3.5.5.7 Output
  • 3.6 Result and Analysis
  • 3.6.1 VGG-16
  • 3.6.2 VGG-19
  • 3.6.3 Custom Model
  • 3.6.4 SVM
  • 3.6.5 Modified CNN
  • 3.7 Activation Maps
  • 3.8 Conclusion
  • References
  • Chapter 4 IoT-Based Micro-Expression Recognition for Nervousness Detection in COVID-Like Condition
  • 4.1 Introduction
  • 4.2 Related Work
  • 4.3 Methodology
  • 4.3.1 Algorithm
  • 4.3.2 Datasets
  • 4.3.3 Performance Parameters
  • 4.4 Experimental Results
  • 4.4.1 Accuracy
  • 4.5 Conclusion
  • References.
  • Chapter 5 Genetically Optimized Computer-Aided Diagnosis for Detection and Classification of COVID-19
  • 5.1 Introduction
  • 5.2 Related Works
  • 5.3 Problem Definition
  • 5.4 Proposed Methodology
  • 5.4.1 Data Acquisition
  • 5.4.2 Preprocessing
  • 5.4.3 Segmentation Using Optimized Region Growing
  • 5.4.4 Feature Extraction and Feature Selection
  • 5.4.5 Two Stage Classification Using Genetically Optimized ANN Classification
  • 5.4.5.1 Stage 1 Classification
  • 5.4.5.2 Stage 2 Classification
  • 5.5 Performance Analysis of the Proposed Methodology
  • 5.5.1 Parameter Settings and Experimental Setup
  • 5.5.2 Segmentation Performance of Proposed Methodology
  • 5.5.3 Classification Performance of the Proposed Approach
  • 5.6 Conclusion
  • References
  • Chapter 6 Micro-Expression Recognition Using 3D-CNN Layering
  • 6.1 Introduction
  • 6.2 Literature Review
  • 6.2.1 Hand-Designed (Manual) Techniques
  • 6.2.2 Learning-Based (Dynamic) Techniques
  • 6.3 Micro-Expressions Datasets
  • 6.3.1 Non-Spontaneous Dataset
  • 6.3.1.1 Polikovsky Dataset
  • 6.3.1.2 USF-HD
  • 6.3.1.3 York DDT
  • 6.3.2 Spontaneous Datasets
  • 6.3.2.1 CASME
  • 6.3.2.2 SMIC
  • 6.3.2.3 CASME II
  • 6.3.2.4 SAMM Dataset
  • 6.3.2.5 CAS(ME)2
  • 6.4 Proposed CNN Models
  • 6.4.1 Primary CNN
  • 6.4.2 Secondary CNN
  • 6.5 Experimental Results and Discussions
  • 6.5.1 Experimental Results
  • 6.5.2 Accuracy Standard Deviation Analysis
  • 6.5.3 Effect of 3D Kernel
  • 6.5.4 Impact of Facial Features
  • 6.6 Conclusion
  • References
  • Chapter 7 Applications of AI, IoT, IoMT, and Biosensing Devices in Curbing COVID-19
  • 7.1 Introduction
  • 7.1.1 Clinical Properties
  • 7.1.2 Mode of Transmission
  • 7.2 Related Work
  • 7.2.1 History of Similar Pandemics
  • 7.2.2 Viruses of the Respiratory Tract
  • 7.3 COVID-19 Diagnosis
  • 7.4 Treatment
  • 7.5 Prevention.
  • 7.6 Internet of Things and Internet of Material Things (IoT and IoMT)
  • 7.7 Artificial Intelligence
  • 7.8 Diagnosis and Screening
  • 7.9 Biosensing Devices Application in COVID-19 Patients
  • 7.10 Cloud Computing Used in COVID-19
  • 7.11 Internet of Things Used in COVID-19
  • 7.12 Conclusion
  • References
  • Chapter 8 How Artificial Intelligence and IoT Aid in Fighting COVID-19
  • 8.1 Introduction
  • 8.1.1 Scope
  • 8.2 Coronavirus Pandemic
  • 8.3 Application of Artificial Intelligence
  • 8.3.1 Prediction
  • 8.3.2 Detection of COVID-19 Using Artificial Intelligence Models
  • 8.3.3 Identification of Potential Vaccines Using Artificial Intelligence
  • 8.4 Conclusion
  • 8.5 Future Work
  • References
  • Chapter 9 Physical Therapy Recommendations for Patients with COVID-19
  • 9.1 Introduction
  • 9.1.1 Learning objectives
  • 9.2 The Effect of Increasing the Aerobic Capacity on Immune and Pulmonary Functions
  • 9.3 Aerobic Exercise Recommendations for Patients with COVID-19
  • 9.4 Pulmonary Exercises Recommendations for Patients with COVID-19
  • References
  • Chapter 10 AI in Fighting against COVID-19: A Case Study
  • 10.1 Introduction
  • 10.2 Symptoms
  • 10.3 Prevention
  • 10.4 Preparation for COVID-19
  • 10.5 Testing for COVID-19
  • 10.6 Formation of Viruses
  • 10.7 Prevention of Viruses through Antioxidants
  • 10.8 Herbs as Antioxidants
  • 10.9 Long-Term Implications
  • 10.10 Case Study of Fish Worker Community
  • 10.11 Artificial Intelligence (AI) and Machine Learning (ML) in COVID-19 [12, 13]
  • 10.12 Artificial Intelligence (AI) Algorithms
  • 10.13 Classification
  • 10.14 Current Reports
  • 10.15 Conclusion
  • References
  • Index.

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